Quest for Interpretability-Accuracy Trade-off Supported by Fingrams into the Fuzzy Modeling Tool GUAJE

Abstract Understand the behavior of Fuzzy Rule-based Systems (FRBSs) at inference level is a complex task that allows the designer to produce simpler and powerful systems. The fuzzy inference-grams –known as fingrams– establish a novel and mighty tool for understanding the structure and behavior of fuzzy systems. Fingrams represent FRBSs as social networks made of nodes representing fuzzy rules and edges representing the degree of interaction between pairs of rules at inference level (no edge means no significant interaction). We can analyze fingrams obtaining helpful information such as detecting potential conflicts between rules, unused rules and redundant ones. This paper introduces a new module for fingram generation and analysis included in the free software tool GUAJE. This tool aims to design, analyze and evaluate fuzzy systems with good interpretability-accuracy trade-off. In addition, GUAJE includes several intuitive and interactive tutorials to uncover the possibilities it offers. One of them ge...

[1]  David P. Pancho,et al.  Enhancing the fuzzy modeling tool GUAJE with a new module for fingrams-based analysis of fuzzy rule bases , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[2]  José M. Alonso,et al.  Highly Interpretable Linguistic Knowledge Bases Optimization: Genetic Tuning versus Solis-Wetts. Looking for a good interpretability-accuracy trade-off , 2007, 2007 IEEE International Fuzzy Systems Conference.

[3]  Brigitte Charnomordic,et al.  Learning interpretable fuzzy inference systems with FisPro , 2011, Inf. Sci..

[4]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[5]  Thorsten Meinl,et al.  KNIME - the Konstanz information miner: version 2.0 and beyond , 2009, SKDD.

[6]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[7]  José M. Alonso,et al.  HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers , 2011, Soft Comput..

[8]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[9]  Athanasios V. Vasilakos,et al.  Autonomous Composition of Fuzzy Granules in Ambient Intelligence Scenarios , 2009, Human-Centric Information Processing Through Granular Modelling.

[10]  José M. Alonso,et al.  Generating Understandable and Accurate Fuzzy Rule-Based Systems in a Java Environment , 2011, WILF.

[11]  Piedad Brox Jiménez,et al.  Using Xfuzzy Environment for the Whole Design of Fuzzy Systems , 2007, 2007 IEEE International Fuzzy Systems Conference.

[12]  Félix de Moya Anegón,et al.  Visualizing the structure of science , 2007 .

[13]  Oscar Cordón,et al.  A new variant of the Pathfinder algorithm to generate large visual science maps in cubic time , 2008, Inf. Process. Manag..

[14]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[15]  David P. Pancho,et al.  Social Network Analysis of Co-fired Fuzzy Rules , 2013, Soft Computing: State of the Art Theory and Novel Applications.

[16]  Luis Magdalena,et al.  Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview , 2003 .

[17]  Dimiter Driankov,et al.  Fuzzy Model Identification , 1997, Springer Berlin Heidelberg.

[18]  J. Casillas.  Interpretability issues in fuzzy modeling , 2003 .

[19]  GuillaumeSerge,et al.  Fuzzy inference systems , 2012 .

[20]  D. W. Dearholt,et al.  Properties of pathfinder networks , 1990 .

[21]  Hidetomo Ichihashi,et al.  Neuro-fuzzy ID3: a method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning , 1996, Fuzzy Sets Syst..

[22]  David P. Pancho,et al.  FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility , 2013, IEEE Transactions on Fuzzy Systems.

[23]  Aníbal Ollero,et al.  Automatic design of fuzzy controllers for car-like autonomous robots , 2004, IEEE Transactions on Fuzzy Systems.

[24]  Christian Borgelt,et al.  FrIDA -A Free Intelligent Data Analysis Toolbox , 2007, 2007 IEEE International Fuzzy Systems Conference.

[25]  Iluminada Baturone,et al.  XFSML: An XML-based modeling language for fuzzy systems , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[26]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[27]  Satoru Kawai,et al.  An Algorithm for Drawing General Undirected Graphs , 1989, Inf. Process. Lett..

[28]  José M. Alonso,et al.  Special issue on interpretable fuzzy systems , 2011, Inf. Sci..

[29]  José M. Alonso,et al.  HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism , 2008, Int. J. Intell. Syst..

[30]  Brigitte Charnomordic,et al.  Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro , 2012, Expert Syst. Appl..

[31]  Eyke Hüllermeier,et al.  Fuzzy methods in machine learning and data mining: Status and prospects , 2005, Fuzzy Sets Syst..

[32]  Michael Spann,et al.  A new approach to clustering , 1990, Pattern Recognit..

[33]  J. M. Alonso,et al.  Analyzing interpretability of fuzzy rule-based systems by means of fuzzy inference-grams , 2011 .

[34]  Michael R. Berthold,et al.  Mixed fuzzy rule formation , 2003, Int. J. Approx. Reason..

[35]  Zaida Chinchilla-Rodríguez,et al.  A new technique for building maps of large scientific domains based on the cocitation of classes and categories , 2004, Scientometrics.

[36]  Brigitte Charnomordic,et al.  Generating an interpretable family of fuzzy partitions from data , 2004, IEEE Transactions on Fuzzy Systems.

[37]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[38]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[39]  Jesús Alcalá-Fdez,et al.  jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation , 2012, 2012 IEEE International Conference on Fuzzy Systems.